It can be formulated using the Virtual Network Embedding (VNE) problem, which was and remains an active field of studies, also known because of its NP-hardness.
Jan 27, 2021 · The simulation results highlight the efficiency of our approach through an increased performance over time, while outperforming state-of-art ...
The key point of this approach is modeling of the VNE problem as an episodic Markov Decision Process which is solved in a Reinforcement Learning fashion ...
On the Use of Graph Neural Networks for Virtual Network Embedding
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... Multiple virtual nodes were able to be embedded onto a single substrate node in their work, which is a close form of virtual network scaling in NFV field.
In this work, we develop GraphVine, a parallelizable VNE solution based on spatial Graph Neural Networks (GNN) that clusters the servers to guide the embedding ...
The simulation results highlight the efficiency of our approach through an increased performance over time, while outperforming state-of-art solutions in terms ...
On the Use of Graph Neural Networks for Virtual Network Embedding.
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On the Use of Graph Neural Networks for Virtual Network Embedding. Resource URI: https://dblp.l3s.de/d2r/resource/publications/conf/isncc/RkhamiQAOR20. Home ...
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Feb 3, 2022 · This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm.
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In recent years, Graph Neural Networks (GNNs) have received considerable attention considering they can directly analyze graphical data. In particular, the ...
Virtual Network Embedding refers to the process of deploying and connecting virtual networks to a physical network in the field of network virtualization.